How is machine learning changing manufacturing?

2nd April 2018

Machine learning techniques have the capability to revolutionise manufacturing in the years to come. As the techniques become more advanced, their capacities will improve and their price of implementation will drop.

The state of the market today

Machine learning is still in its infancy, having burst onto the scene in 2012 and grown steadily since. Trendforce predicts the market for 'smart manufacturing solutions' will grow to nearly AUD $420 billion by 2020.

How is machine learning changing manufacturing?

Machine learning, however difficult to develop, is fairly easy to explain. Instead of programming computers in a linear fashion with a set of rules that guide its operation, machine learning algorithms learn from data that they're fed, without being told exactly what to do.

A commonly application of machine learning, for example, is in computer vision or perception. As Michael Mendelson of the NVIDIA Deep Learning Institute told Redshift, "without flexible algorithms, computers can only do what we tell them. Many tasks, especially those involving perception, can't be translated into rule-based instructions. In a manufacturing context, some of the more immediately interesting applications will involve perception." Elements such as quality control, where a product needs to be checked for defects, may well become the common providence of machine learning algorithms in the future.

Although the future is where we'll likely see machine learning applied more often, some companies are already employing it extensively. Siemens is a conglomerate that's been using machine learning techniques to monitor their factories for several years now. GE is another company that heavily utilises machine learning. Their system "Predix" takes the data generated from its factories and uses its deep learning capacities to spot potential issues and provide solutions.

What's stopping more companies adopting machine learning techniques?

A study by Infosys asked manufacturers what the hurdles were in their drive towards digital transformation. They cited a lack of:

Data-led insights on demand (67 per cent),

Collaboration among teams (51 per cent),

Time (40 per cent).

When asked specifically what stood in the way of adopting more AI-supported elements as part of their digital transformation strategies, they said they lacked:

In-house knowledge and skills around the technology (58 per cent),

Clarity around the AI value proposition (57 per cent),

Financial resources (54 per cent).

While it's clear machine learning has a lot of potential in the manufacturing space, there are still many hurdles standing in the way of companies adopting it.

For more information on how Advanced Business Manager's software solutions can take your manufacturing operation to the next level, contact us today.